Ambient noise reduction in cerebral near infrared spectroscopy based on frequency division multiplexing
Bibliographic record
Abstract
Near-Infrared Spectroscopy (NIRS) is a non-invasive technique, extensively used to monitor the hemodynamic variations in cerebral neuronal tissues. For cerebral NIRS, the back-scattering probe is more prevailing, in which an incident beam is diffused, and only a slight fraction of the source optical energy reaches the light detectors. Multiplexing in the time domain is the conventional method used to distinguish the optical density of each NIR source at the receiver site. Even though time-multiplexing is straightforward and convenient, the ambient light can significantly contaminate the NIR beams during the sampling-path from the source to the detector. In this work, we present a novel method based on frequency division multiplexing (FDM) to overcome the interference of ambient light even without an external optical filter. The method proposes to modulate the NIR source intensities by using specific carrier frequencies distinct from the dominant frequency components of ambient light intensity. By modulating the intensity of each NIR source, and applying them at their specific frequency channels, the receiver is capable of distinguishing the received optical signals based on their frequency channel. Because the frequency channels are adjusted at distinct dominant frequency components of the ambient intensity, the latter ambient noise can be filtered out instantly. The method has been implemented by using electronic circuit design and evaluated both by numerical simulation and experimental measurements. The signal to noise ratio (SNR) has been improved at least by 45dB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".